CES-530: Collaborative Brain-Computer Interface for Aiding Decision-making

We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a simple matching task involving the rapid sequential presentation of pairs of visual patterns and the subsequent decision as whether the two patterns in a pair were the same or different. We recorded the response times of observers as well as a neural feature which predicts incorrect decisions and, thus, indirectly indicates the confidence of the decisions made by the observers. We then built a composite neuro-behavioural feature which optimally combines the two measures. For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features. Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule. An analysis of event-related potentials indicates that substantial differences are present in the proximity of the response for correct and incorrect trials, further corroborating the idea of using hybrids of brain-computer interfaces and traditional strategies for improving decision making

A Collaborative BCI Trained to Aid Group Decisions in a Visual Search Task Works Well with Similar Tasks

This study tests the possibility of using collaborative brain-computer interfaces (cBCIs) trained with EEG data collected during a decision task to enhance group performance in similar tasks

Analogue P300-based BCI pointing device

We propose a P300-based BCI mouse. The system is analogue: the pointer is controlled by directly combining the amplitudes of the outputs produced by a filter in the presence of different stimuli. The system is optimised by a genetic algorithm

Augmenting Group Performance in Target-Face Recognition via Collaborative Brain-Computer Interfaces for Surveillance Applications

This paper presents a hybrid collaborative brain- computer interface (cBCI) to improve group-based recognition of target faces in crowded scenes recorded from surveillance cameras. The cBCI uses a combination of neural features extracted from EEG and response times to estimate the decision confidence of the users. Group decisions are then obtained by weighing individual responses according to these confidence estimates. Results obtained with 10 participants indicate that the proposed cBCI improves decision errors by up to 7% over traditional group decisions based on majority. Moreover, the confidence estimates obtained by the cBCI are more accurate and robust than the confidence reported by the participants after each decision. These results show that cBCIs can be an effective means of human augmentation in realistic scenarios

Possible sources of perceptual errors in P300-based speller paradigm

Some perceptual phenomena can interfere with character identification in Farwell and Donchin's P300-based speller paradigm: attentional blink, repetition blindness and other effects caused by attentional limits. In the paper we discuss these and provide empirical evidence for one class of perceptual errors

A novel onset detection technique for brain?computer interfaces using sound-production related cognitive tasks in simulated-online system

Objective. Self-paced EEG-based BCIs (SP-BCIs) have traditionally been avoided due to two sources of uncertainty: (1) precisely when an intentional command is sent by the brain, i.e., the command onset detection problem, and (2) how different the intentional command is when compared to non-specific (or idle) states. Performance evaluation is also a problem and there are no suitable standard metrics available. In this paper we attempted to tackle these issues. Approach. Self-paced covert sound-production cognitive tasks (i.e., high pitch and siren-like sounds) were used to distinguish between intentional commands (IC) and idle states. The IC states were chosen for their ease of execution and negligible overlap with common cognitive states. Band power and a digital wavelet transform were used for feature extraction, and the Davies?Bouldin index was used for feature selection. Classification was performed using linear discriminant analysis. Main results. Performance was evaluated under offline and simulated-online conditions. For the latter, a performance score called true-false-positive (TFP) rate, ranging from 0 (poor) to 100 (perfect), was created to take into account both classification performance and onset timing errors. Averaging the results from the best performing IC task for all seven participants, an 77.7% true-positive (TP) rate was achieved in offline testing. For simulated-online analysis the best IC average TFP score was 76.67% (87.61% TP rate, 4.05% false-positive rate). Significance. Results were promising when compared to previous IC onset detection studies using motor imagery, in which best TP rates were reported as 72.0% and 79.7%, and which, crucially, did not take timing errors into account. Moreover, based on our literature review, there is no previous covert sound-production onset detection system for spBCIs. Results showed that the proposed onset detection technique and TFP performance metric have good potential for use in SP-BCIs

Implications of the Use of Grazing Sheep on Kiwi Fruit Orchard

In the southern part of Brazil there is an important area of kiwi fruit, mainly cultivated by small farmers. The use of sheep under trees of kiwi fruits could be an interesting alternative for small farmers to reduce their mowing costs, to improve their income and to provide meat for the farmers\u27 family. However there is a lack of information about the damage that the animals could cause to kiwi fruit plants. The objective of this study was to monitor the effect of the use of sheep on a kiwi fruit orchard